CN103714004A - JVM online memory leak analysis method and system - Google Patents
JVM online memory leak analysis method and system Download PDFInfo
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- CN103714004A CN103714004A CN201410002172.2A CN201410002172A CN103714004A CN 103714004 A CN103714004 A CN 103714004A CN 201410002172 A CN201410002172 A CN 201410002172A CN 103714004 A CN103714004 A CN 103714004A
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Abstract
The invention provides a JVM online memory leak analysis method and system. The method comprises the following steps that node information in a reference relationship tree is obtained, and the node information comprises the number of objects and the size of space occupied by nodes; node information in the reference relationship tree is obtained again after a preset period, and a corresponding reference relationship changing tree is constructed according to the node information obtained twice; JVM memory leak is analyzed online according to the reference relationship changing tree. By means of the JVM online memory leak analysis method and system, the JVM memory leak condition can be analyzed online precisely.
Description
Technical field
The present invention relates to field of computer technology, be specifically related to the online RAM leakage analytical approach of a kind of JVM and the online RAM leakage analytic system of a kind of JVM.
Background technology
JVM(Java Virtual Machine, Java Virtual Machine) in operational process, often there is RAM leakage situation.Traditional RAM leakage analytical approach is generally: OOM(Out Of Memory is occurring, and internal memory overflows) or manually after dump, by analyzing dump file, analyze RAM leakage situation.
In addition, in JVM operational process, can, by the adduction relationship tree number in Visualvm, JProfile monitoring java virutal machine memory, size etc., can know roughly online the information that JVM internal memory overflows.But these information are too general, accurate object and the situation of change thereof in monitoring analysis virtual machine.
Summary of the invention
Based on this, the invention provides the online RAM leakage analytical approach of a kind of JVM and system, can analyze accurately the situation of JVM RAM leakage.
For achieving the above object, the present invention adopts following technical scheme:
The online RAM leakage analytical approach of JVM, comprises the following steps:
Obtain each nodal information in adduction relationship tree, described nodal information comprises the quantity of object and the size that node takes up room;
After the schedule time, again obtain each nodal information in described adduction relationship tree, and according to the nodal information obtaining for twice, build corresponding adduction relationship and change tree;
According to described adduction relationship, change tree on-line analysis JVM RAM leakage.
The online RAM leakage analytic system of JVM, comprising:
Acquisition module, for obtaining each nodal information of adduction relationship tree, described nodal information comprises the quantity of object and the size that node takes up room;
Build module, for again obtain described each nodal information of adduction relationship tree after the schedule time, and according to the nodal information obtaining for twice, build adduction relationship and change tree;
Analysis module, for changing tree on-line analysis JVM RAM leakage according to described adduction relationship.
In sum, the online RAM leakage analytical approach of a kind of JVM of the present invention and system, by obtaining respectively each nodal information in the adduction relationship book before and after the schedule time, according to the nodal information obtaining for twice, build corresponding adduction relationship and change tree, and change according to this adduction relationship the leak case that tree carries out on-line analysis internal memory.The solution of the present invention by reference relationship change tree can be within each time period the situation of on-line analysis JVM RAM leakage, therefore can not only initial stage of development accurately on-line analysis JVM whether there is RAM leakage, and can also before there is OOM, send early warning and tentatively search problem.
Accompanying drawing explanation
Fig. 1 is the schematic flow sheet of the online RAM leakage analytical approach of a kind of JVM in the embodiment of the present invention;
Fig. 2 is the schematic diagram of the benchmark adduction relationship tree in the embodiment of the present invention;
Fig. 3 is the schematic diagram of the adduction relationship tree after the variation in the embodiment of the present invention;
Fig. 4 is that the adduction relationship in the embodiment of the present invention changes the schematic diagram of tree;
Fig. 5 is the online RAM leakage analytic system of a kind of JVM schematic diagram in the embodiment of the present invention.
Embodiment
In order to make object of the present invention, technical scheme and advantage clearer, below in conjunction with drawings and Examples, the present invention is further elaborated.Should be appreciated that specific embodiment described herein, only in order to explain the present invention, is not intended to limit the present invention.
Shown in Figure 1, the online RAM leakage analytical approach of a kind of JVM, comprises the following steps:
Step S101, obtains each nodal information in adduction relationship tree, and described nodal information comprises the quantity of object and the size that node takes up room.The adduction relationship tree obtaining in this step can be the benchmark adduction relationship tree of object, and initialization is applied relational tree to this.The process and the full GC that obtain an object reference relational tree are similar.In conjunction with shown in Figure 2 from, GC Root starts downward search, sets up adduction relationship tree, in an adduction relationship node, if there are identical tired a plurality of objects, by figure denote object number, and records total size that this node node takes up room.In setting up adduction relationship process, recurrence is quoted not record, thus due to object all can quote java.lang.Class, therefore do not need to be recorded in adduction relationship yet.Be denoted as shown in Figure 2 Object B(2,258) node, the instance objects of representation class B has two, taking total memory headroom is 258.
Step S102 again obtains each nodal information in described adduction relationship tree after the schedule time, and according to the nodal information obtaining for twice, builds corresponding adduction relationship and change tree.After the time interval of setting or under user operates triggering (as user checks), again obtain described adduction relationship tree, there is variation in adduction relationship tree relative datum adduction relationship tree now.The adduction relationship tree having changed by analysis, and and step S101 in the adduction relationship tree that obtains compare calculating, according to the result of calculating, obtain adduction relationship and change tree.The adduction relationship tree having changed is abandoned, and preserve adduction relationship variation tree.
Step S103, changes tree on-line analysis JVM RAM leakage according to described adduction relationship.
As a good embodiment, describedly according to the nodal information obtaining for twice, build the process that adduction relationship changes tree and specifically can comprise the following steps:
Step S1021, subtracts the adduction relationship tree obtaining before the schedule time with interval and calculates again obtain described adduction relationship tree after the schedule time.As when building adduction relationship variation tree, by the adduction relationship tree having changed, deduct benchmark adduction relationship tree and produce.Described adduction relationship changes tree and also by root node, is set out, till newly-increased leaf node.
Step S1022, obtains the change information of each nodal information according to result of calculation.For example in the contrast of two adduction relationships tree, node is unchanged but while having newly-increased child node, adduction relationship records this change information in changing, for example, can and take up room Object node number and be 0.Adduction relationship tree is changed to Fig. 3 adduction relationship tree at certain time intervals afterwards as shown in Figure 2.And the newly-increased reference object object D of node Object A, root node newly-increased reference object Object B, its reference object Object E in two adduction relationship trees.
Step S1023, builds adduction relationship according to described change information and changes tree.For example above-mentioned newly-increased node can be added adduction relationship to change in tree, in node object, record object variation part.Build adduction relationship and change tree, the change information obtaining after the adduction relationship tree shown in Fig. 2 and Fig. 3 is calculated adds adduction relationship to change in tree.As shown in Figure 4, node Object A is unchanged but have newly-increased child node, adduction relationship records this node Object A(0 in changing, 0), newly-increased node Object D, Object E add in in-tree, and Object B increases an object, takies 62 byte spaces, record Object B(1,62).
As a good embodiment, the process that changes tree on-line analysis JVM RAM leakage according to described adduction relationship specifically can comprise the following steps:
Adduction relationship in the schedule time is changed to each node in tree to be analyzed;
According to analysis result, obtain object variation trend map in adduction relationship tree, can realize on-line analysis object variation trend; Described analysis result can comprise that adduction relationship changes the number of objects of each node in burl and the space taking;
According to object predetermined in described object variation trend map, carry out on-line analysis JVM memory overflow.
Owing to may having more object variation in virtual machine, so predetermine one can refer to the object of user's appointment or change maximum node objects.By can analyze these to the variation of predetermine one, whether whether still need to stay virutal machine memory because RAM leakage causes, thereby realize online RAM leakage analysis.
As a good embodiment, the process that changes tree on-line analysis RAM leakage according to described adduction relationship specifically can also comprise the following steps:
According to described object variation trend map, judge whether to exist RAM leakage;
If so, there is memory overflow in explanation, can search and quote path and memory overflow point according to the adduction relationship that changes object; Otherwise there is not memory overflow, continue adduction relationship tree to analyze.By checking changing the analysis of the adduction relationship of object, can locate and quote path and RAM leakage point.To the object continuing to increase, can may there is RAM leakage and finally can cause the OOM of virtual machine by this point of anticipation, having sent in advance early warning.For example, comparison diagram 2 and the application relational tree shown in Fig. 3, the object of node Object B is by (2,258) be varied to (3,20), the object of known Object B has increased by one, takes up room 62, total space 86(that increases is because the Object E that B quotes quotes without other, count B gross space), at this moment between in interval, as the reason of object B without this variation, can judge that RAM leakage has occurred object B, thus orientation problem.
Corresponding with the online RAM leakage analytical approach of a kind of JVM in above-described embodiment, embodiments of the invention also provide the online RAM leakage analytic system of a kind of JVM, as shown in Figure 5, comprising:
Acquisition module 101, for obtaining each nodal information of adduction relationship tree, described nodal information comprises the quantity of object and the size that node takes up room;
Build module 102, for again obtain described each nodal information of adduction relationship tree after the schedule time, and according to the nodal information obtaining for twice, build adduction relationship and change tree;
Analysis module 103, for changing tree on-line analysis JVM RAM leakage according to described adduction relationship.
As a good embodiment, described structure module specifically can comprise:
Computing module, for calculating with the adduction relationship tree obtaining before the schedule time at interval again obtain described adduction relationship tree after the schedule time;
Change information acquisition module, for obtaining the change information of each nodal information according to result of calculation;
Build submodule, for building adduction relationship according to described change information, change tree.
As a good embodiment, described analysis module specifically can also comprise:
First analyzes submodule, changes each node of tree analyze for the adduction relationship in the schedule time;
Trend map acquisition module, for obtaining each node object variation trend map according to analysis result;
Second analyzes submodule, for carrying out on-line analysis JVM memory overflow according to the predetermined object of described object variation trend map.
As a good embodiment, described analysis module specifically can also comprise:
Judge module, for judging whether to exist RAM leakage according to described object variation trend map;
Search module, for when judgment result is that of described judge module is, according to the adduction relationship inquiry that changes object, quote path and memory overflow point.
Other technical characterictic of the online RAM leakage analytic system of above-mentioned a kind of JVM is identical with the online RAM leakage analytical approach of a kind of JVM of the present invention, and it will not go into details herein.
By above scheme, can find out, the online RAM leakage analytical approach of a kind of JVM and the system of the embodiment of the present invention, by obtaining respectively each nodal information in the adduction relationship book before and after the schedule time, according to the nodal information obtaining for twice, build corresponding adduction relationship and change tree, and change according to this adduction relationship the leak case that tree carries out on-line analysis internal memory.The solution of the present invention by reference relationship change tree can be within each time period the situation of on-line analysis JVM RAM leakage, therefore can not only initial stage of development accurately on-line analysis JVM whether there is RAM leakage, and can also before there is OOM, send early warning and tentatively search problem.
It should be noted that, unless context separately has the description of specific distinct, the element in the present invention and assembly, the form that quantity both can be single exists, and form that also can be a plurality of exists, and the present invention does not limit this.In addition, although the step in the present invention is arranged with label, but and be not used in the precedence that limits step, unless expressly stated the order of step or the execution of certain step need other steps as benchmark, otherwise the relative order of step is adjustable.
The above embodiment has only expressed several embodiment of the present invention, and it describes comparatively concrete and detailed, but can not therefore be interpreted as the restriction to the scope of the claims of the present invention.It should be pointed out that for the person of ordinary skill of the art, without departing from the inventive concept of the premise, can also make some distortion and improvement, these all belong to protection scope of the present invention.Therefore, the protection domain of patent of the present invention should be as the criterion with claims.
Claims (8)
1. the online RAM leakage analytical approach of JVM, is characterized in that, comprises the following steps:
Obtain each nodal information in adduction relationship tree, described nodal information comprises the quantity of object and the size that node takes up room;
After the schedule time, again obtain each nodal information in described adduction relationship tree, and according to the nodal information obtaining for twice, build corresponding adduction relationship and change tree;
According to described adduction relationship, change tree on-line analysis JVM RAM leakage.
2. the online RAM leakage analytical approach of JVM according to claim 1, is characterized in that, the process that changes tree on-line analysis JVM RAM leakage according to described adduction relationship comprises the following steps:
Adduction relationship in the schedule time is changed to each node in tree to be analyzed;
According to analysis result, obtain object variation trend map in adduction relationship tree;
According to object predetermined in described object variation trend map, carry out on-line analysis JVM memory overflow.
3. the online RAM leakage analytical approach of JVM according to claim 2, is characterized in that, the process that changes tree on-line analysis RAM leakage according to described adduction relationship is further comprising the steps of:
According to described object variation trend map, judge whether to exist RAM leakage;
If so, according to the adduction relationship that changes object, search and quote path and memory overflow point.
4. the online RAM leakage analytical approach of JVM according to claim 1, is characterized in that, describedly according to the nodal information obtaining for twice, builds the process that adduction relationship changes tree and comprises the following steps:
By again obtain described adduction relationship tree after the schedule time, subtract the adduction relationship tree obtaining before the schedule time with interval and calculate;
According to result of calculation, obtain the change information of each nodal information;
According to described change information, build adduction relationship and change tree.
5. the online RAM leakage analytic system of JVM, is characterized in that, comprising:
Acquisition module, for obtaining each nodal information of adduction relationship tree, described nodal information comprises the quantity of object and the size that node takes up room;
Build module, for again obtain described each nodal information of adduction relationship tree after the schedule time, and according to the nodal information obtaining for twice, build adduction relationship and change tree;
Analysis module, for changing tree on-line analysis JVM RAM leakage according to described adduction relationship.
6. the online RAM leakage analytic system of JVM according to claim 5, is characterized in that, described analysis module comprises:
First analyzes submodule, changes each node of tree analyze for the adduction relationship in the schedule time;
Trend map acquisition module, for obtaining each node object variation trend map according to analysis result;
Second analyzes submodule, for carrying out on-line analysis JVM memory overflow according to the predetermined object of described object variation trend map.
7. the online RAM leakage analytic system of JVM according to claim 6, is characterized in that, described analysis module also comprises:
Judge module, for judging whether to exist RAM leakage according to described object variation trend map;
Search module, for when judgment result is that of described judge module is, according to the adduction relationship inquiry that changes object, quote path and memory overflow point.
8. the online RAM leakage analytic system of JVM according to claim 5, is characterized in that, described structure module comprises:
Computing module, for calculating with the adduction relationship tree obtaining before the schedule time at interval again obtain described adduction relationship tree after the schedule time;
Change information acquisition module, for obtaining the change information of each nodal information according to result of calculation;
Build submodule, for building adduction relationship according to described change information, change tree.
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Cited By (14)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105204996A (en) * | 2015-09-24 | 2015-12-30 | 努比亚技术有限公司 | Memory leak detection device and method and terminal |
| CN105630662A (en) * | 2014-10-28 | 2016-06-01 | 腾讯科技（深圳）有限公司 | Memory detection method and apparatus |
| CN105790992A (en) * | 2014-12-25 | 2016-07-20 | 深圳Tcl数字技术有限公司 | Method and device for managing network node information |
| CN107102922A (en) * | 2017-04-01 | 2017-08-29 | 北京三快在线科技有限公司 | Internal-memory detection method, device and electronic equipment |
| CN108073441A (en) * | 2016-11-14 | 2018-05-25 | 阿里巴巴集团控股有限公司 | A kind of virutal machine memory monitoring and managing method and equipment |
| CN108073439A (en) * | 2016-11-11 | 2018-05-25 | 深圳业拓讯通信科技有限公司 | A kind of JVM RAM leakages automatic testing method and system |
| CN108664390A (en) * | 2017-03-31 | 2018-10-16 | 华为技术有限公司 | A kind of method for detecting memory leakage and equipment |
| CN109032827A (en) * | 2018-07-03 | 2018-12-18 | 郑州云海信息技术有限公司 | It is a kind of to track the test macro and method for leading to memory overflow exception |
| CN110502891A (en) * | 2019-08-08 | 2019-11-26 | 北京字节跳动网络技术有限公司 | A kind of method, apparatus, medium and electronic equipment obtaining proceeding internal memory leakage |
| CN110888712A (en) * | 2019-10-10 | 2020-03-17 | 望海康信(北京)科技股份公司 | Java virtual machine optimization method and system |
| CN111352829A (en) * | 2019-11-21 | 2020-06-30 | 杭州迪普科技股份有限公司 | Memory leak test method, device and equipment |
| CN111352734A (en) * | 2020-02-26 | 2020-06-30 | 烽火通信科技股份有限公司 | Memory leak detection method and memory management module |
| CN111858068A (en) * | 2020-07-31 | 2020-10-30 | 重庆紫光华山智安科技有限公司 | Method and device for positioning Java program performance problem |
| CN111858339A (en) * | 2020-07-22 | 2020-10-30 | 腾讯科技(成都)有限公司 | Memory analysis method, device and system, computing device and computer-readable storage medium |
Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050268286A1 (en) * | 2004-04-12 | 2005-12-01 | Motoki Obata | Method of detecting memory leak causing portion and execution program thereof |
| US20080301646A1 (en) * | 2007-05-29 | 2008-12-04 | Satish Chandra Gupta | Detecting Dangling Pointers and Memory Leaks Within Software |
| CN101710303A (en) * | 2009-12-01 | 2010-05-19 | 中国人民解放军国防科学技术大学 | Memory leakage detecting method based on flow sensitivity and context sensitivity directing picture |
| CN102148844A (en) * | 2010-02-09 | 2011-08-10 | 深圳市金蝶中间件有限公司 | Memory leak positioning method, server, client and system |
-
2014
- 2014-01-02 CN CN201410002172.2A patent/CN103714004A/en active Pending
Patent Citations (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US20050268286A1 (en) * | 2004-04-12 | 2005-12-01 | Motoki Obata | Method of detecting memory leak causing portion and execution program thereof |
| US20080301646A1 (en) * | 2007-05-29 | 2008-12-04 | Satish Chandra Gupta | Detecting Dangling Pointers and Memory Leaks Within Software |
| CN101710303A (en) * | 2009-12-01 | 2010-05-19 | 中国人民解放军国防科学技术大学 | Memory leakage detecting method based on flow sensitivity and context sensitivity directing picture |
| CN102148844A (en) * | 2010-02-09 | 2011-08-10 | 深圳市金蝶中间件有限公司 | Memory leak positioning method, server, client and system |
Non-Patent Citations (1)
| Title |
|---|
| 查伟: ""关于 Java内存泄漏问题的研究"", 《咸宁学院学报》 * |
Cited By (18)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN105630662A (en) * | 2014-10-28 | 2016-06-01 | 腾讯科技（深圳）有限公司 | Memory detection method and apparatus |
| CN105630662B (en) * | 2014-10-28 | 2019-07-19 | 腾讯科技（深圳）有限公司 | Internal-memory detection method and device |
| CN105790992A (en) * | 2014-12-25 | 2016-07-20 | 深圳Tcl数字技术有限公司 | Method and device for managing network node information |
| CN105204996A (en) * | 2015-09-24 | 2015-12-30 | 努比亚技术有限公司 | Memory leak detection device and method and terminal |
| CN108073439A (en) * | 2016-11-11 | 2018-05-25 | 深圳业拓讯通信科技有限公司 | A kind of JVM RAM leakages automatic testing method and system |
| CN108073441A (en) * | 2016-11-14 | 2018-05-25 | 阿里巴巴集团控股有限公司 | A kind of virutal machine memory monitoring and managing method and equipment |
| CN108073441B (en) * | 2016-11-14 | 2022-05-10 | 阿里巴巴集团控股有限公司 | Virtual machine memory supervision method and equipment |
| CN108664390A (en) * | 2017-03-31 | 2018-10-16 | 华为技术有限公司 | A kind of method for detecting memory leakage and equipment |
| CN107102922A (en) * | 2017-04-01 | 2017-08-29 | 北京三快在线科技有限公司 | Internal-memory detection method, device and electronic equipment |
| CN109032827A (en) * | 2018-07-03 | 2018-12-18 | 郑州云海信息技术有限公司 | It is a kind of to track the test macro and method for leading to memory overflow exception |
| CN110502891A (en) * | 2019-08-08 | 2019-11-26 | 北京字节跳动网络技术有限公司 | A kind of method, apparatus, medium and electronic equipment obtaining proceeding internal memory leakage |
| CN110888712A (en) * | 2019-10-10 | 2020-03-17 | 望海康信(北京)科技股份公司 | Java virtual machine optimization method and system |
| CN110888712B (en) * | 2019-10-10 | 2022-10-11 | 望海康信(北京)科技股份公司 | Java virtual machine optimization method and system |
| CN111352829A (en) * | 2019-11-21 | 2020-06-30 | 杭州迪普科技股份有限公司 | Memory leak test method, device and equipment |
| CN111352734A (en) * | 2020-02-26 | 2020-06-30 | 烽火通信科技股份有限公司 | Memory leak detection method and memory management module |
| CN111858339A (en) * | 2020-07-22 | 2020-10-30 | 腾讯科技(成都)有限公司 | Memory analysis method, device and system, computing device and computer-readable storage medium |
| CN111858339B (en) * | 2020-07-22 | 2023-11-28 | 腾讯科技(成都)有限公司 | Memory analysis method, device and system, computing device and computer readable storage medium |
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Application publication date: 20140409 |